🚀 go-pugleaf

                 C A L L  F O R  P A R T I C I P A T I O N S

                 AI'94 TUTORIAL ON CONSTRAINT-BASED REASONING

				   by

                             Hans Werner Guesgen
                         Computer Science Department
                          University of Auckland
                             Private Bag 92019
                           Auckland, New Zealand
                       Email: hans@cs.auckland.ac.nz

			       22 November 1994


ABSTRACT

Many topics in AI became fashionable and then disappeared again.
Constraint-based reasoning is certainly an exception to this. Started
in the early seventies with algorithms like the well-known Waltz
algorithm, the interest in constraint-based reasoning has increased
during the successive years, involving researchers from different
areas and with different backgrounds.

Constraint-based reasoning is applied in a variety of applications. To
name a few examples: A project group at the University of Hamburg
recently developed a constraint-based system for planning the cabin
layout of the newest version of the Airbus (which is considered to be
one of the most advanced civil aircrafts currently available).
Researchers from NASA recently published their work on a
constraint-based system for scheduling observations using the Hubble
Space Telescope.  Ascent Technology, Inc., developed the
constraint-based system ARIS (Airport Resource Information System),
which is used by Delta Airlines to help allocate airport gates to
arriving flights.


DETAILED PLAN

This tutorial will give an introduction to the main concepts and
techniques of constraint-based reasoning. It is divided into a lecture
and a practical part. The latter involves some programming using a
toolbox of constraint satisfaction algorithms written in C. The
lecture is structured as follows:

   1. Motivation for the tutorial and a general introduction to
      constraint-based reasoning.

   2. Presentation of a demo domain for constraint-based reasoning:
      reasoning about the spatial relationships among items in a
      typical office.

   3. Introduction of a general framework for constraint-based
      reasoning.

   4. Overspecified constraint satisfaction problems: a real-world
      problem.

   5. General techniques for constraint-based reasoning: from local
      consistency algorithms to backtracking with heuristics.

   6. Constraint-based reasoning on parallel and massively parallel
      computers.

   7. Constraint satisfaction as an optimization problem.

   8. Some applications of constraint-based reasoning.

   9. Summary of the main concepts and conclusion.


Recommended reading material is:

   H.W. Guesgen and J. Hertzberg.
   A Perspective of Constraint-Based Reasoning.
   Lecture Notes in Artificial Intelligence 597.
   Springer, Berlin, Germany, 1992.


BIO-DATA OF THE PRESENTER

The presenter is a lecturer at the Computer Science Department of the University
of Auckland, New Zealand, where he is teaching introductory and advance courses on AI.

He is co-chairing the Workshop on Constraint Processing to be held in 
connection with the 1994 European Conference on Artificial Intelligence 
(ECAI-94) in Amsterdam, The Netherlands. Together with Joachim Hertzberg from 
the GMD in St. Augustin, Germany, he presented tutorials on constraint-based 
reasoning at the British AI conference in 1991 (AISB-91) and at the German AI 
conference in 1991 (GWAI-91).  He is the author/coauthor of two books on 
constraint-based reasoning, and has more than 30 publications in books, 
journals, and conference proceedings.


PREREQUISITES

Participants should have basic knowledge of AI and be able to write
simple programs in C.

-------------------------------------------------------------------------------

       Seventh Australian Joint Conference on Artificial Intelligence (AI'94) 

                       STRUCTURED SEQUENCE TUTORIALS

A structured sequence of pre-conference tutorials on several aspects of
applied AI has been organised. Tutorial participants will be able to select
from a choice of tutorials to suit their specialist requirements.

The following are the list of tutorials organised for AI'94. All
participants of any of these tutorials may attend the talk entitled
"Introduction to Artificial Intelligence". This is a complementary session
for all tutorial participants.
                    

Guide:
(y) - indicate YES
(n) - indicate NO
(t) - indicate THEORETICAL SESSION
(p) - indicate PRACTICAL SESSION
(d) - indicate DEMONSTRATION SESSION
1hr - indicate one hour
4s  - indicate four sessions
3s  - indicate three sessions 

Note: Each session is One and a Half hours long.


--------------------------------------------------------------------------------
No.     Tutorial Title               Presenters            CODE Length Practical
--------------------------------------------------------------------------------
[0] Introduction To Artificial       Not Confirmed yet     AI     1hr      -
    Intelligence

[1] Constraint-Based Reasoning       Dr. H. W. Guesgen     CBR    4s       y

[2] Fundamentals of Fuzzy Logic      Dr. A. Sekercioglu    FLFC   3s       y
    and Fuzzy Logic Controllers      G.K. Egan
               
[3] An Introduction to Evolutionary  Dr. X. Yao            EC     3s       y
    Computation                      Prof Z. Michalewicz
               
[4] Building Intelligent Decision    Dr. J. Zeleznikow     IDSS   3s       y
    Support Systems through the use  Mr. Dan Hunter
    of multiple reasonig Strategies
                
[5] Intelligent Learning Database    Dr Xindong Wu         ILDB   3s       y 
    Systems            

[6] Nonmonotonic Reasoning           Dr. M-A Williams      NMR    3s       y
                                     Dr. G. Antoniou

[7] Theoretical Foundations of       Dr. A. G. Hoffman     TFML   3s       n
    Machine Learning                 Dr. Shyam Kapur
                                     Dr. Arun Sharma

[8] Knowledge Acquisition and        Dr. P. Compton        KAM    3s       d
    Maintenance with Ripple Down 
    Rules     

[9] Hybrid (AI symbolic,             Dr. Nik K. Kasabov    HS     3s       d
    Connectionist, Fuzzy, Chaotic) 
    Systems


--------------------------------------------------------------------------------
Tentative Tutorial Timetable
--------------------------------------------------------------------------------

Monday 21/11/94 (Day 1)
=======================
 9.30 - 10.30:  Introduction to AI
10.30 - 11.00:  Morning Tea Break
11.00 - 12.30:  NMR(t)  IDSS(t) EC(t)   TFML(t)
12.30 -  2.00:  Lunch
 2.00 -  3.30:  NMR(t)  IDSS(t) EC(t)   TFML(t)
 3.30 -  4.00:  Afternoon Tea Break     
 4.00 -  5.30:  NMR(p)  IDDS(p) EC(p)   TFML(t)

Tuesday 22/11/94 (Day 2)
========================
 9.00 - 10.30:  CBR(t)  ILDB(t) FLFC(t) KAM(t)  HS(t)
10.30 - 11.00:  Morning Tea Break
11.00 - 12.30:  CBR(t)  ILDB(t) FLFC(t) KAM(t)  HS(t)
12.30 -  2.00:  Lunch
 2.00 -  3.30:  CBR(t)  ILDB(p) FLFC(p) KAM(d)  HS(d)   
 3.30 -  4.00:  Afternoon Tea Break
 4.00 -  5.30:  CBR(p)                               

--------------------------------------------------------------------------------
Examples of structured sequence of tutorials:
--------------------------------------------------------------------------------

There are couple of structured sequence of tutorials that one could adopt.
For example, if the participant is interested in logic/theoretical basis of
AI, then he/she may want to select the following sequence:

Day 1   9.30 - 10.30: Introdution to AI
       11.00 - 12.30: NMR(t)
        2.00 -  3.30: NMR(t)
        4.00 -  5.30: NMR(p)

Day 2   9.00 - 10.30: CBR(t)    
       11.00 - 12.30: CBR(t)
        2.00 -  3.30: CBR(t) 
        4.00 -  5.30: CBR(p)

Alternatively, if the participant is more interested in the applications of
AI, then the following sequence may be more suitable:

Day 1   9.30 - 10.30: Introdution to AI
       11.00 - 12.30: IDSS(t)
        2.00 -  3.30: IDSS(t)
        4.00 -  5.30: IDSS(p)

Day 2   9.00 - 10.30: FLFC(t)
       11.00 - 12.30: FLFC(t)
        2.00 -  3.30: FLFC(p) 


If the interest is in Machine Learning/Knowledge Acquisition, then the
possible sequence may be:

Day 1   9.30 - 10.30: Introdution to AI 
       11.00 - 12.30: TFML(t)
        2.00 -  3.30: TFML(t)
        4.00 -  5.30: TFML(t)

Day 2   9.00 - 10.30: ILDB(t)  or  KAM(t)    
       11.00 - 12.30: ILDB(t)  or  KAM(t)
        2.00 -  3.30: ILDB(p)  or  KAM(d)

Another possible sequence may be:

Day 1   9.30 - 10.30: Introdution to AI
       11.00 - 12.30: EC(t)
        2.00 -  3.30: EC(t)
        4.00 -  5.30: EC(p)

Day 2   9.00 - 10.30: HS(t)    
       11.00 - 12.30: HS(t)
        2.00 -  3.30: HS(d)
--------------------------------------------------------------------------------
Further Information:
--------------------------------------------------------------------------------
For further information on the structured sequence tutorials, please
contact the AI'94 Tutorial Co-ordinator, at the following address:

                        Dr. Dickson Lukose
         Department of Mathematics, Statistics, and Computing Science
                    University of New England
                        Armidale, N.S.W., 2351
                             AUSTRALIA

                 e-mail: ai94@fermat.une.edu.au
                 fax.  : (+61 67) 73 3312
--------------------------------------------------------------------------------